DBMS > Amazon Redshift vs. Apache Impala vs. Firebase Realtime Database vs. GreptimeDB

System Properties Comparison Amazon Redshift vs. Apache Impala vs. Firebase Realtime Database vs. GreptimeDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Primary database model

Relational DBMS

Document store

Time Series DBMS

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Website

aws.amazon.com/redshift

impala.apache.org

firebase.google.com/products/realtime-database

greptime.com

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

firebase.google.com/docs/database

docs.greptime.com

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

acquired by Google 2014

Greptime Inc.

Initial release

2012

2013

2012

2022

Current release

4.1.0, June 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Rust

Server operating systems

hosted

Linux

hosted

Android
Docker
FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

gRPC
HTTP API
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript
Objective-C

C++
Erlang
Go
Java
JavaScript

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

limited functionality with using 'rules'

Python

Triggers

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

yes, based on authentication and database rules

Simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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